Advancing Understanding of Just-in-Time States for Supporting Physical Activity (Project JustWalk JITAI): Protocol for a System ID Study of Just-in-Time Adaptive Interventions.
Advancing Understanding of Just-in-Time States for Supporting Physical Activity (Project JustWalk JITAI): Protocol for a System ID Study of Just-in-Time Adaptive Interventions.
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DOI：
10.2196/52161
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发表时间：
2023-09-26
影响因子：
1.7
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中科院分区：
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适时适应性干预(JITAI)旨在当个人愿意接受并能对提示作出有益反应时提供支持。即时(JIT)状态的概念对于JITAI至关重要。到目前为止，JIT状态要么是以数据驱动的方式制定的，要么是仅基于理论的。需要一种能够对JIT状态概念进行严格的理论测试和优化的方法。本系统ID实验的目的是对JIT状态进行经验性研究，并对旨在增加体力活动的JITAI进行经验优化(步数/天)。我们招募了年龄在≥25岁、身体不活跃、讲英语、拥有智能手机的成年人。参与者佩戴了Fitbit Versa 3，并使用了270天的研究应用。Just Walk Jitai项目使用系统ID方法来研究JIT状态。具体地说，在不同理论上可行的联合调查组国家的运作中，提供的支助有系统地不同，以便能够对这一概念进行更严格和系统的研究。我们在实验中改变了两种干预成分：每天发送多达4次的通知，旨在增加一个人在接下来的3小时内的步数，并建议每天的步数目标。步行通知是在JIT状态的不同操作中提供的，考虑到需要(即，是否以前达到了每天的步骤目标)、机会(例如，接下来的3小时是否是一个人以前步行的时间窗口)和接受性(即，一个人以前在收到通知后步行)。建议的每日步数目标在与个人每天步数基线水平相关的范围内有系统地变化(例如，4000步)，直到达到有临床意义的目标(例如，平均每天8000步作为一个周期的较低门槛)。将使用一系列系统ID估计方法来分析数据，并获得面向控制的动态模型来研究JIT状态。将对所有方法的估计模型进行对比，最终目标是指导JIT状态的严格、可复制的经验制定和研究，以指导未来的JITAI。正如系统ID中常见的那样，我们进行了一系列模拟研究来制定实验。我们的模拟研究结果表明，这种方法在生成用于研究JIT状态的信息和唯一数据方面是可信的。这项研究于2022年6月开始招募参与者，最终招募了48名参与者。数据收集工作于2023年4月结束。分析完成后，这项研究的结果预计将于2023年第四季度提交发表。这项研究将是对JIT状态的第一次经验性调查，它使用系统ID方法来为体力活动的可扩展JITAI的优化提供信息。ClinicalTrials.gov NCT05273437；https://clinicaltrials.gov/ct2/show/NCT05273437 dr1-10.2196/52161
Just-in-time adaptive interventions (JITAIs) are designed to provide support when individuals are receptive and can respond beneficially to the prompt. The notion of a just-in-time (JIT) state is critical for JITAIs. To date, JIT states have been formulated either in a largely data-driven way or based on theory alone. There is a need for an approach that enables rigorous theory testing and optimization of the JIT state concept. The purpose of this system ID experiment was to investigate JIT states empirically and enable the empirical optimization of a JITAI intended to increase physical activity (steps/d). We recruited physically inactive English-speaking adults aged ≥25 years who owned smartphones. Participants wore a Fitbit Versa 3 and used the study app for 270 days. The JustWalk JITAI project uses system ID methods to study JIT states. Specifically, provision of support systematically varied across different theoretically plausible operationalizations of JIT states to enable a more rigorous and systematic study of the concept. We experimentally varied 2 intervention components: notifications delivered up to 4 times per day designed to increase a person’s steps within the next 3 hours and suggested daily step goals. Notifications to walk were experimentally provided across varied operationalizations of JIT states accounting for need (ie, whether daily step goals were previously met or not), opportunity (ie, whether the next 3 h were a time window during which a person had previously walked), and receptivity (ie, a person previously walked after receiving notifications). Suggested daily step goals varied systematically within a range related to a person’s baseline level of steps per day (eg, 4000) until they met clinically meaningful targets (eg, averaging 8000 steps/d as the lower threshold across a cycle). A series of system ID estimation approaches will be used to analyze the data and obtain control-oriented dynamical models to study JIT states. The estimated models from all approaches will be contrasted, with the ultimate goal of guiding rigorous, replicable, empirical formulation and study of JIT states to inform a future JITAI. As is common in system ID, we conducted a series of simulation studies to formulate the experiment. The results of our simulation studies illustrated the plausibility of this approach for generating informative and unique data for studying JIT states. The study began enrolling participants in June 2022, with a final enrollment of 48 participants. Data collection concluded in April 2023. Upon completion of the analyses, the results of this study are expected to be submitted for publication in the fourth quarter of 2023. This study will be the first empirical investigation of JIT states that uses system ID methods to inform the optimization of a scalable JITAI for physical activity. ClinicalTrials.gov NCT05273437; https://clinicaltrials.gov/ct2/show/NCT05273437 DERR1-10.2196/52161
DOI：
10.1177/1090198113496787
发表时间：
2013-10
期刊：
Health education & behavior : the official publication of the Society for Public Health Education
影响因子：
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作者：
Hekler EB;Buman MP;Poothakandiyil N;Rivera DE;Dzierzewski JM;Morgan AA;McCrae CS;Roberts BL;Marsiske M;Giacobbi PR Jr
通讯作者：
Giacobbi PR Jr
DOI：
10.23919/acc53348.2022.9867350
发表时间：
2022-06
期刊：
Proceedings of the ... American Control Conference. American Control Conference
影响因子：
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